
"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a       :						Optional[Any]       		=		{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a       :						int       		=		['''ConvBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a       :						Dict       		=		[
     '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ConvBertForMaskedLM''',
     '''ConvBertForMultipleChoice''',
     '''ConvBertForQuestionAnswering''',
     '''ConvBertForSequenceClassification''',
     '''ConvBertForTokenClassification''',
     '''ConvBertLayer''',
     '''ConvBertModel''',
     '''ConvBertPreTrainedModel''',
     '''load_tf_weights_in_convbert''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a       :						List[Any]       		=		[
     '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFConvBertForMaskedLM''',
     '''TFConvBertForMultipleChoice''',
     '''TFConvBertForQuestionAnswering''',
     '''TFConvBertForSequenceClassification''',
     '''TFConvBertForTokenClassification''',
     '''TFConvBertLayer''',
     '''TFConvBertModel''',
     '''TFConvBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
 from .tokenization_convbert import ConvBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_convbert_fast import ConvBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convbert import (
      CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvBertForMaskedLM,
      ConvBertForMultipleChoice,
      ConvBertForQuestionAnswering,
      ConvBertForSequenceClassification,
      ConvBertForTokenClassification,
      ConvBertLayer,
      ConvBertModel,
      ConvBertPreTrainedModel,
      load_tf_weights_in_convbert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convbert import (
      TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertLayer,
      TFConvBertModel,
      TFConvBertPreTrainedModel,
  )


else:
 import sys

 a       :						str       		=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

"""simple docstring"""



import os
from math import logaa


def 			_UpperCamelCase						(    _A = "base_exp.txt"   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = 0
    _UpperCAmelCase        = 0
    for i, line in enumerate(open(os.path.join(os.path.dirname(_A   )       ,   _A   )   )   ):
        _UpperCAmelCase     ,_UpperCAmelCase        = list(map(_A       ,   line.split(""","""   )   )   )
        if x * logaa(_A   ) > largest:
            _UpperCAmelCase        = x * logaa(_A   )
            _UpperCAmelCase        = i + 1
    return result


if __name__ == "__main__":
 print(solution())